Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 274-278.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Face Recognition Using SPCA and HOG with Single Training Image Per Person

HAN Xu1, CHEN Hai-yun1, WANG Yi2, XU Jin1   

  1. School of Electrical Engineering and Information,Southwest Petroleum University,Nanchong,Sichuan 637001,China1;
    School of Electronic and Information Engineering,Liaoning University of Engineering and Technology,Huludao,Liaoning 125105,China2
  • Online:2019-06-14 Published:2019-07-02

Abstract: Face recognition based on single sample is a challenging task.This paper combined the Similar Principal Component Analysis (SPCA) algorithm and Histograms of Oriented Gradients (HOG) algorithm,and used SPCA to screen out the similar information of the image class,and quantified the similar information blocks with HOG algorithm to make the two advantages complementary.Finally,we used Pearson correlation (PC) to identify similarity and conduct experiments on the Extended Yale B database.Experimental results show that the proposed algorithm has better recognition performance than traditional algorithm when the illumination of the face image changes.

Key words: Face recognition, Histograms of oriented gradients(HOG), Pearson correlation (PC), Similar principal component analysis (SPCA)

CLC Number: 

  • TP391
[1]左腾.人脸识别技术综述[J].软件导刊,2017(2):182-185.
[2]肖梦佳.基于图像识别的施工现场智能监控系统的相关技术研究与实现[D].成都:电子科技大学,2016.
[3]马园园.人脸识别技术与考勤系统应用研究[D].南京:南京邮电大学,2017.
[4]何志威,李军.基于人脸识别的移动终端考勤系统的设计[J].福建电脑,2018,34(3):19-20.
[5]杨秀坤,岳新启,汲清波.基于HOG和DMMA的单样本人脸识别[J].计算机应用研究,2015(2):627-629.
[6]杨恢先,翟云龙,蔡勇勇,等.基于中心对称梯度幅值相位模式的单样本人脸识别[J].光电子·激光,2015(5):969-977.
[7]覃磊,李德华,周康.基于QR分解与2DLDA的单样本人脸识别[J].微电子学与计算机,2015(2):65-68.
[8]YANG M,VAN L,ZHANG L.Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person[C]∥2013 IEEE International Conference on Computer Vision(ICCV).IEEE,2013:689-696.
[9]韩旭,刘强,许瑾,谌海云.基于伪PCA的手写数字识别算法[J].计算机科学,2018,45(S2):278-281,307.
[10]DALAL,NAVNEET,TRIGGS,et al.Histograms of Oriented Gradients for Human Detection[C]∥IEEE International Conference on Computer Vision and Pattern Recognition,CVPR 2005.2005:886-893.
[11]RAHMAN N A.A course in theoretical statistics for sixth forms,technical colleges,colleges of education,universities[M].Charles Griffin & Company Limited,1969.
[12]BUDA A,JARYNOWSKI A.Life time of correlations and its applications[M].ABRASCO-Associação Brasileira de Saúde Coletiva,2010:459-470.
[13]https://en.wikipedia.org/wiki/Pearson_correlation_coefficient.
[14]http://www.realstatistics.com/correlation/basic-concepts-correlation.
[15]JR J S.The Relationship between the Coefficient of Correlation and the Angle included between Regression Lines[J].Journal of Educational Research,1947,41(4):311-313.
[16]http://www.hawaii.edu/powerkills/UC.HTM.
[17]LEE K C,HO J,KRIEGMAN D J.Acquiring Linear Subspaces for Face Recognition under Variable Lighting[J].IEEE Tran-sactions on Pattern Analysis & Machine Intelligence,2005,27(5):684-698.
[18]GEORGHIADES A S,BELHUMEUR P N,KRIEGMAN D J.From few to many:illumination cone models for face recognition under variable lighting and pose[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2002,23(6):643-660.
[19]CORTES C,VAPNIK V.Support vector machines[J].Machine Learning,1995,20:273-293.
[20]COVER T,HART P.Nearest neighbor pattern classification [J].IEEE Trans.inf.theory,1967,13(1):21-27.
[21]WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2008,31(2):210-227.
[22]LOUGHERY J.Making FLDA applicable to face recognition with one sample per person[J].Pattern Recognition,2004,37(7):1553-1555.
[23]ZHANG L,YANG M,FENG X.Sparse representation or collaborative representation:Which helps face recognition?[C]∥IEEE International Conference on Computer Vision(ICCV 2011).Bacelona,Spain:IEEE,2011:471-478.
[24]SU Y,SHAN S,CHEN X,et al.Adaptive generic learning for face recognition from a single sample per person[C]∥Computer Vision and Pattern Recognition.IEEE,2010:2699-2706.
[25]LU J,TAN Y P,WANG G.Discriminative Multimanifold Ana-lysis for Face Recognition from a Single Training Sample per Person[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2012,35(1):39-51.
[26]KUMAR R,BANERJEE A,VEMURI B C,et al.Maximizing all margins:Pushing face recognition with Kernel Plurality[C]∥International Conference on Computer Vision.IEEE,2011:2375-2382.
[27]DENG W,HU J,GUO J.Extended SRC:Undersampled Face Recognition via Intraclass Variant Dictionary[J].IEEE Tran-sactions on Pattern Analysis & Machine Intelligence,2012,34(9):1864-1870.
[28]ZHU P,ZHANG L,HU Q,et al.Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization[C]∥European Conference on Computer Vision.IEEE,2012:822-835.
[29]ZHU P,YANG M,ZHANG L,et al.Local Generic Representation for Face Recognition with Single Sample per Person[M].Springer International Publishing,2014:41-50.
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